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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Hive

Hive provides a SQL layer abstraction over the MapReduce framework with several optimizations. This is needed because of the complexity of writing code using the MapReduce framework. For example, a simple count of the records in a specific file takes at least a few dozen lines of code, which is not productive to anyone. Hive abstracts the MapReduce code by encapsulating the logic from the SQL statement into a MapReduce framework code, which is automatically generated and executed on the backend. This saves incredible amounts of time for anyone who needs to spend more time on doing something useful with the data, rather than going through the boiler plate coding for every single task that needs to be executed and every single computation that's desired as part of your job:

Hive is not designed for online transaction processing and does not offer real-time queries and...

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